In recent years, the adoption of containerization solutions has grown significantly. Simultaneously, there has been a rise in applications with stricter latency and jitter requirements, such as tactile internet, autonomous vehicles, and immersive AR/VR. Time-Sensitive Networking (TSN) emerges as a promising solution for these latency-critical applications, offering tools specifically designed to manage such conditions and support mixed-criticality scenarios involving both hard and soft deadlines. This paper explores techniques and solutions for running latencysensitive applications in containerized environments. It focuses on how high-priority traffic can coexist with best-effort traffic using TSN mechanisms, and how containerized applications can leverage hardware-based techniques to achieve precise transmission timing. The results demonstrate that, with TSN mechanisms alone on the network side, high-priority traffic can coexist with interfering traffic while maintaining accurate transmission scheduling and the target bandwidth. However, hardware-based techniques such as Launchtime proved crucial in achieving the most precise transmission schedule-evident in a $\mathbf{4 2}$-fold reduction in standard deviation. This improvement is also reflected in the P99 metric, where 99% of transmission delays remained below 0.501 ms. Finally, the paper presents mitigation strategies to address challenges introduced by container isolation, which can cause unforeseen conflicts between transmission schedules and add latency to communications.

This paper presents a method for processing sparse, non-Gaussian multimodal data in a simultaneous localization and mapping (SLAM) framework using factor graphs. Our approach demonstrates the feasibility of using a sum-product inference strategy to recover functional belief marginals from highly non-Gaussian situations, relaxing the prolific unimodal Gaussian assumption. The method is more focused than conventional multi-hypothesis approaches, but still captures dominant modes via multi-modality. The proposed algorithm exists in a trade space that spans the anticipated uncertainty of measurement data, task-specific performance, sensor quality, and computational cost. This work leverages several major algorithm design constructs, including clique recycling, to put an upper bound on the allowable computational expense - a major challenge in non-parametric methods. To better demonstrate robustness, experimental results show the feasibility of the method on at least two of four major sources of non-Gaussian behavior: i) the first introduces a canonical range-only problem which is always underdetermined although composed exclusively from Gaussian measurements; ii) a real-world AUV dataset, demonstrating how ambiguous acoustic correlator measurements are directly incorporated into a non-Gaussian SLAM solution, while using dead reckon tethering to overcome short term computational requirements.

Synthetic Aperture Sonar (SAS) is a technique to improve the spatial resolution from a moving set of receivers by extending the array in time, increasing the effective array length and aperture. This technique is limited by the accuracy of the receiver position estimates, necessitating highly accurate, typically expensive aided-inertial navigation systems for submerged platforms. We leverage simultaneous localization and mapping to fuse acoustic and navigational measurements and obtain accurate pose estimates even without the benefit of absolute positioning for lengthy underwater missions. We demonstrate a method of formulating the well-known SAS problem in a SLAM framework, using acoustic data from hydrophones to simultaneously estimate platform and beacon position. An empirical probability distribution is computed from a conventional beamformer to correctly account for uncertainty in the acoustic measurements. The non-parametric method relieves the familiar Gaussian-only assumption currently used in the localization and mapping discipline and fits effectively into a factor graph formulation with conventional factors such as ground-truth priors and odometry. We present results from field experiments performed on the Charles River with an autonomous surface vehicle which demonstrate simultaneous localization of an unknown acoustic beacon and vehicle positioning, and provide comparison to GPS ground truths.

We extend the core operation of non-parametric belief propagation (NBP), also known as multi-scale sequential Gibbs sampling, to approximate products of kernel density estimated beliefs that reside on some manifold. The original algorithm, though multidimensional, implicitly assumes the beliefs to reside on the Euclidean ℝdspace only. The proposed extension generalizes to any mixture of Riemannian manifolds, provided the primary operations - addition and subtraction- are defined. Our motivation is primarily focused on state-estimation using non-Gaussian factor graphs for multimodal simultaneous localization and mapping in robotics. The paper presents the method as well as simulation and experimental results for validation. Our implementation is publicly available and allows for expansion with user-defined manifold mixtures.

Typically, the reconstruction problem is addressed in three independent steps: first, sensor processing techniques are used to filter and segment sensor data as required by the front end. Second, the front end builds the factor graph for the problem to obtain an accurate estimate of the robot's full trajectory. Finally, the end product is obtained by further processing of sensor data, now re-projected from the optimized trajectory. In this paper we present an approach to model the reconstruction problem in a way that unifies the aforementioned problems under a single framework for a particular application: sonar-based inspection of underwater structures. This is achieved by formulating both the sonar segmentation and point cloud reconstruction problems as factor graphs, in tandem with the SLAM problem. We provide experimental results using data from a ship hull inspection test.

From archaeology to the inspection of subsea structures, underwater mapping has become critical to many applications. Because of the balanced trade-off between range and resolution, multibeam sonars are often used as the primary sensor in underwater mapping platforms. These sonars output an image representing the intensity of the received acoustic echos over space, which must be classified into free and occupied regions before range measurements are determined and spatially registered. Most classifiers found in the underwater mapping literature use local thresholding techniques, which are highly sensitive to noise, outliers, and sonar artifacts typically found in these images. In this paper we present an overview of some of the techniques developed in the scope of our work on sonar-based underwater mapping, with the aim of improving map accuracy through better segmentation performance. We also provide experimental results using data collected with a DIDSON imaging sonar that show that these techniques improve both segmentation accuracy and robustness to outliers.

We propose a submap-based technique for mapping of underwater structures with complex geometries. Our approach relies on the use of probabilistic volumetric techniques to create submaps from multibeam sonar scans, as these offer increased outlier robustness. Special attention is paid to the problem of denoising/enhancing sonar data. Pairwise submap alignment constraints are used in a factor graph framework to correct for navigation drift and improve map accuracy. We provide experimental results obtained from the inspection of the running gear and bulbous bow of a 600-foot, Wright-class supply ship.